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Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics.
Gónzalez-Bandala, Daniel Alejandro; Cuevas-Tello, Juan Carlos; Noyola, Daniel E; Comas-García, Andreu; García-Sepúlveda, Christian A.
Afiliação
  • Gónzalez-Bandala DA; Engineering Faculty, UASLP, San Luis Potosí 78290, Mexico.
  • Cuevas-Tello JC; Commerce and Administration Faculty, UAT, Tamaulipas 87000, Mexico.
  • Noyola DE; Engineering Faculty, UASLP, San Luis Potosí 78290, Mexico.
  • Comas-García A; Microbiology Department, Medicine Faculty, UASLP, San Luis Potosí 78290, Mexico.
  • García-Sepúlveda CA; Microbiology Department, Medicine Faculty, UASLP, San Luis Potosí 78290, Mexico.
Article em En | MEDLINE | ID: mdl-32599746
ABSTRACT
The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Respiratórias / Doenças Transmissíveis / Epidemias Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Mexico Idioma: En Revista: Int J Environ Res Public Health Ano de publicação: 2020 Tipo de documento: Article País de afiliação: México

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Respiratórias / Doenças Transmissíveis / Epidemias Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Mexico Idioma: En Revista: Int J Environ Res Public Health Ano de publicação: 2020 Tipo de documento: Article País de afiliação: México